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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- """task distill script"""
-
- import os
- import argparse
- from mindspore import context
- from mindspore.train.model import Model
- from mindspore.nn.optim import AdamWeightDecay
- from mindspore import set_seed
- from src.dataset import create_dataset
- from src.utils import StepCallBack, ModelSaveCkpt, EvalCallBack, BertLearningRate
- from src.config import train_cfg, eval_cfg, teacher_net_cfg, student_net_cfg, task_cfg
- from src.cell_wrapper import BertNetworkWithLoss, BertTrainCell
-
- WEIGHTS_NAME = 'eval_model.ckpt'
- EVAL_DATA_NAME = 'eval.tf_record'
- TRAIN_DATA_NAME = 'train.tf_record'
-
-
- def parse_args():
- """
- parse args
- """
- parser = argparse.ArgumentParser(description='ternarybert task distill')
- parser.add_argument('--device_target', type=str, default='GPU', choices=['Ascend', 'GPU'],
- help='Device where the code will be implemented. (Default: GPU)')
- parser.add_argument('--do_eval', type=str, default='true', choices=['true', 'false'],
- help='Do eval task during training or not. (Default: true)')
- parser.add_argument('--epoch_size', type=int, default=3, help='Epoch size for train phase. (Default: 3)')
- parser.add_argument('--device_id', type=int, default=0, help='Device id. (Default: 0)')
- parser.add_argument('--do_shuffle', type=str, default='true', choices=['true', 'false'],
- help='Enable shuffle for train dataset. (Default: true)')
- parser.add_argument('--enable_data_sink', type=str, default='true', choices=['true', 'false'],
- help='Enable data sink. (Default: true)')
- parser.add_argument('--save_ckpt_step', type=int, default=50,
- help='If do_eval is false, the checkpoint will be saved every save_ckpt_step. (Default: 50)')
- parser.add_argument('--eval_ckpt_step', type=int, default=50,
- help='If do_eval is true, the evaluation will be ran every eval_ckpt_step. (Default: 50)')
- parser.add_argument('--max_ckpt_num', type=int, default=10,
- help='The number of checkpoints will not be larger than max_ckpt_num. (Default: 10)')
- parser.add_argument('--data_sink_steps', type=int, default=1, help='Sink steps for each epoch. (Default: 1)')
- parser.add_argument('--teacher_model_dir', type=str, default='', help='The checkpoint directory of teacher model.')
- parser.add_argument('--student_model_dir', type=str, default='', help='The checkpoint directory of student model.')
- parser.add_argument('--data_dir', type=str, default='', help='Data directory.')
- parser.add_argument('--output_dir', type=str, default='./', help='The output checkpoint directory.')
- parser.add_argument('--task_name', type=str, default='sts-b', choices=['sts-b', 'qnli', 'mnli'],
- help='The name of the task to train. (Default: sts-b)')
- parser.add_argument('--dataset_type', type=str, default='tfrecord', choices=['tfrecord', 'mindrecord'],
- help='The name of the task to train. (Default: tfrecord)')
- parser.add_argument('--seed', type=int, default=1, help='The random seed')
- parser.add_argument('--train_batch_size', type=int, default=16, help='Batch size for training')
- parser.add_argument('--eval_batch_size', type=int, default=32, help='Eval Batch size in callback')
- return parser.parse_args()
-
-
- def run_task_distill(args_opt):
- """
- run task distill
- """
- task = task_cfg[args_opt.task_name]
- teacher_net_cfg.seq_length = task.seq_length
- student_net_cfg.seq_length = task.seq_length
- train_cfg.batch_size = args_opt.train_batch_size
- eval_cfg.batch_size = args_opt.eval_batch_size
- teacher_ckpt = os.path.join(args_opt.teacher_model_dir, args_opt.task_name, WEIGHTS_NAME)
- student_ckpt = os.path.join(args_opt.student_model_dir, args_opt.task_name, WEIGHTS_NAME)
- train_data_dir = os.path.join(args_opt.data_dir, args_opt.task_name, TRAIN_DATA_NAME)
- eval_data_dir = os.path.join(args_opt.data_dir, args_opt.task_name, EVAL_DATA_NAME)
- save_ckpt_dir = os.path.join(args_opt.output_dir, args_opt.task_name)
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args.device_id)
-
- rank = 0
- device_num = 1
- train_dataset = create_dataset(batch_size=train_cfg.batch_size,
- device_num=device_num,
- rank=rank,
- do_shuffle=args_opt.do_shuffle,
- data_dir=train_data_dir,
- data_type=args_opt.dataset_type,
- seq_length=task.seq_length,
- task_type=task.task_type,
- drop_remainder=True)
- dataset_size = train_dataset.get_dataset_size()
- print('train dataset size:', dataset_size)
- eval_dataset = create_dataset(batch_size=eval_cfg.batch_size,
- device_num=device_num,
- rank=rank,
- do_shuffle=args_opt.do_shuffle,
- data_dir=eval_data_dir,
- data_type=args_opt.dataset_type,
- seq_length=task.seq_length,
- task_type=task.task_type,
- drop_remainder=False)
- print('eval dataset size:', eval_dataset.get_dataset_size())
-
- if args_opt.enable_data_sink == 'true':
- repeat_count = args_opt.epoch_size * dataset_size // args_opt.data_sink_steps
- else:
- repeat_count = args_opt.epoch_size
-
- netwithloss = BertNetworkWithLoss(teacher_config=teacher_net_cfg, teacher_ckpt=teacher_ckpt,
- student_config=student_net_cfg, student_ckpt=student_ckpt,
- is_training=True, task_type=task.task_type, num_labels=task.num_labels)
- params = netwithloss.trainable_params()
- optimizer_cfg = train_cfg.optimizer_cfg
- lr_schedule = BertLearningRate(learning_rate=optimizer_cfg.AdamWeightDecay.learning_rate,
- end_learning_rate=optimizer_cfg.AdamWeightDecay.end_learning_rate,
- warmup_steps=int(dataset_size * args_opt.epoch_size *
- optimizer_cfg.AdamWeightDecay.warmup_ratio),
- decay_steps=int(dataset_size * args_opt.epoch_size),
- power=optimizer_cfg.AdamWeightDecay.power)
- decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
- other_params = list(filter(lambda x: not optimizer_cfg.AdamWeightDecay.decay_filter(x), params))
- group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
- {'params': other_params, 'weight_decay': 0.0},
- {'order_params': params}]
-
- optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps)
-
- netwithgrads = BertTrainCell(netwithloss, optimizer=optimizer)
-
- if args_opt.do_eval == 'true':
- eval_dataset = list(eval_dataset.create_dict_iterator())
- callback = [EvalCallBack(network=netwithloss.bert,
- dataset=eval_dataset,
- eval_ckpt_step=args_opt.eval_ckpt_step,
- save_ckpt_dir=save_ckpt_dir,
- embedding_bits=student_net_cfg.embedding_bits,
- weight_bits=student_net_cfg.weight_bits,
- clip_value=student_net_cfg.weight_clip_value,
- metrics=task.metrics)]
- else:
- callback = [StepCallBack(),
- ModelSaveCkpt(network=netwithloss.bert,
- save_ckpt_step=args_opt.save_ckpt_step,
- max_ckpt_num=args_opt.max_ckpt_num,
- output_dir=save_ckpt_dir,
- embedding_bits=student_net_cfg.embedding_bits,
- weight_bits=student_net_cfg.weight_bits,
- clip_value=student_net_cfg.weight_clip_value)]
- model = Model(netwithgrads)
- model.train(repeat_count, train_dataset, callbacks=callback,
- dataset_sink_mode=(args_opt.enable_data_sink == 'true'),
- sink_size=args_opt.data_sink_steps)
-
-
- if __name__ == '__main__':
- args = parse_args()
- set_seed(args.seed)
- run_task_distill(args)
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